A Deep Learning Approach to Vegetation Images Recognition in Buildings: a Hyperparameter Tuning Case Study

Tuning of data augmentation hyperparameters in deep learning to building construction image classification with small datasets

“…Deep Learning methods can also be applied in recognition of vegetation in building facades images [ 32 ]. In fact, the growth of biological manifestations on building facades may indicate the deterioration and degradation of constructions [ 2 , 24 ].…”

“…In addition, the detection of this pathology in inspection images can assist in the conservation of historic buildings [ 7 , 21 , 24 , 37 ]. In this sense, [ 32 ] proposes a Deep Learning approach for recognizing vegetation in buildings. Another possibility is to use Deep Learning analysis of roof structures [ 33 ].…”

“…For example, in a recent study, [ 33 ] proposes a methodology to tuning of two hyperparameters (learning rate and optimizer) of Neural Networks in the building roof image classification. It is also worth noting that, one of the relevant factors on [ 32 ] and [ 33 ] was the experiments with Data Augmentation. [ 32 ] verified the improvement in validation accuracy when using Data Augmentation to increase the training database.…”

“…It is also worth noting that, one of the relevant factors on [ 32 ] and [ 33 ] was the experiments with Data Augmentation. [ 32 ] verified the improvement in validation accuracy when using Data Augmentation to increase the training database.…”

“…The objective of this paper is to propose a rigorous methodology for tuning of Data Augmentation hyperparameters in Deep Learning to building construction image classification with small data sets. For this, two case studies are observed: vegetation recognition in facades [ 32 ] and roofs structures analysis [ 33 ]. In order to do that, Logistic Regression models [ 16 ] will be used to analyze the performance of Convolutional Neural Networks (CNN) [ 4 , 9 ] trained from 128 combinations of transformations in the images.…”